Big Data Analytics

Real-Time Streaming — Act on Data as It Happens

Real-time streaming transforms data from a historical record into an operational nerve system — enabling immediate responses, live dashboards, and streaming AI acting on events the moment they occur.

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Capabilities

Real-Time Streaming Analytics — deep expertise

Apache Kafka

Kafka cluster design, topic architecture, consumer group management, Schema Registry, and Kafka Connect for high-throughput event streaming.

Apache KafkaConfluentMSKSchema Registry

Apache Flink

Stateful stream processing — complex event patterns, windowed aggregations, exactly-once processing, and real-time ML feature computation.

Apache FlinkFlink SQLStateful ProcessingWindowing

Real-Time OLAP

Streaming analytics pipelines feeding live dashboards — Apache Druid, ClickHouse, and Apache Pinot for sub-second query response on billions of events.

Apache DruidClickHouseApache PinotReal-Time OLAP

Time-Series Platforms

InfluxDB, TimescaleDB, and QuestDB for high-frequency IoT sensor data, financial market data, and operational metrics at extreme ingestion rates.

InfluxDBTimescaleDBQuestDBIoT Data

Streaming ML Inference

Real-time ML scoring on streaming data — fraud detection, anomaly detection, and personalization at sub-50ms latency.

Online InferenceFeature ServingModel ServingLatency SLAs

Change Data Capture

CDC using Debezium, AWS DMS, and Oracle GoldenGate — streaming database changes to lakes, warehouses, and consumers in real time.

DebeziumAWS DMSGoldenGateCDC Patterns

Streaming Results

Real-Time Streaming at Massive Scale

1B+

Events processed per day

<50ms

End-to-end streaming latency

99.99%

Kafka platform availability

10×

Faster operational response time

Our Approach

From Batch Delays to Event-Driven Architecture

Event Modeling

Define your event taxonomy, payload schemas, producer/consumer contracts, and retention policies.

Platform Setup

Deploy and configure your streaming platform (Kafka, Pulsar, or Kinesis) with HA and security settings.

Producer & Consumer

Build producers, consumers, and stream processing applications with schema validation and monitoring.

Operations

Tune for throughput and latency, implement consumer lag alerts, and establish capacity planning reviews.